Function Of Novel Proteins Research Articles

Spindle Assembly and Mitosis in Plants.

In contrast to well-studied fungal and animal cells, plant cells assemble bipolar spindles that exhibit a great deal of plasticity in the absence of structurally defined microtubule-organizing centers like the centrosome. While plants employ some evolutionarily conserved proteins to regulate spindle morphogenesis and remodeling, many essential spindle assembly factors found in vertebrates are either missing or not required for producing the plant bipolar microtubule array. Plants also produce proteins distantly related to their fungal and animal counterparts to regulate critical events such as the spindle assembly checkpoint. Plant spindle assembly initiates with microtubule nucleation on the nuclear envelope followed by bipolarization into the prophase spindle. After nuclear envelope breakdown, kinetochore fibers are assembled and unified into the spindle apparatus with convergent poles. Of note, compared to fungal and animal systems, relatively little is known about how plant cells remodel the spindle microtubule array during anaphase. Uncovering mitotic functions of novel proteins for spindle assembly in plants will illuminate both common and divergent mechanisms employed by different eukaryotic organisms to segregate genetic materials.

Degrons of yeast and mammalian ornithine decarboxylase enzymes make potent combination for regulated targeted protein degradation.

Elevation of polyamine levels in eukaryotes leads to rapid degradation of ornithine decarboxylase (ODC), the first enzyme of polyamine biosynthesis pathway. ODC in yeast (yODC) has two domains, the Nα/β domain consisting of α/β barrel domain (α/β) preceded by an overhang of 50 residues at its N-terminus (N50) and β sheet domain at its C-terminus. Two degradation determinant signals or degrons in yODC sequence, namely the N50 and the antizyme-binding element (AzBE) housed in the α/β domain, are responsible for its degradation by proteasomes. Antizyme (Az) induced under polyamine excess binds to AzBE and delivers ODC to proteasome, while the N50 threads the protein into proteasome. It was previously reported by us that the peptide Nα/β of yODC acts as an independent transplantable degron, whose action can be modulated with the help of antizyme by varying polyamine levels. Mammalian ODC (mODC), in spite of its 40% sequence homology with yODC, is devoid of N50 of yODC and instead sports a C-terminal tail of 37 residues (CmODC). CmODC acts as an independent transplantable degron with no equivalent in yODC. The present study investigates the merits of employing the two degrons Nα/β and CmODC together for targeted protein degradation by expressing them in a chimeric fusion with green fluorescent protein (GFP). Our results establish that under the regulation of antizyme, the signals Nα/β and CmODC acting together enhance degradation better than either degron in isolation. The combination of Nα/β and CmODC can be employed to study the function of novel proteins through their rapid removal.

Engineering degrons of yeast ornithine decarboxylase as vehicles for efficient targeted protein degradation

BackgroundOrnithine decarboxylase (ODC), which catalyzes the first step of polyamine biosynthesis, undergoes rapid targeted degradation (TPD) with the help of its two degron sequences, namely the N-terminal 50 residues (N50) and α/β domain (α/β) housing antizyme binding element (AzBE), in response to increased polyamine levels. Antizyme binds to AzBE of ODC and delivers it to proteasome for degradation. Entire ODC was used as a tag to demonstrate TPD of chimeric proteins. MethodsHere we fashioned three peptide sequences from yeast ODC to test their capability to act as degrons, namely N50, α/β and Nα/β (a combination of N50 and α/β), and monitored their degradation potentials in chimeric proteins. We have examined the correlation between degradation potentials and structural integrity of the peptides, to find mechanistic explanations. ResultsNα/β with two signals in tandem is a better degron, under the regulation of antizyme. N50 like N44 reported earlier could drive chimeric proteins to degradation, while α/β could not act as an independent degron. Strong correlation was observed between functional efficacy of the peptides and their structural integrity. N50, which was believed to be unstructured, displayed propensity for helical conformation. Nα/β exhibited optimal structure, while α/β failed to adopt native like conformation. Conclusions and general significanceFunctional efficacy of the degron Nα/β is a consequence of its structural integrity. Nα/β and N50 could target chimeric proteins to degradation. However, α/β failed in the quest. Nα/β, regulated by antizyme, is better suited than N50 for TPD to understand the function of novel proteins.

Mass Spectrometry-Based Proteomics Identification of Enteropathogenic Escherichia coli Pedestal Constituents.

Enteropathogenic Escherichia coli (EPEC) co-opt host signaling pathways and recruit numerous host proteins to motile morphological structures, called pedestals, at sites of bacterial attachment. These pedestals are hallmarks of EPEC-based disease, and the identification and characterization of the functions of pedestal proteins continue to steadily increase. To identify additional constituents in an unbiased manner, we developed a strategy where EPEC pedestals were elongated artificially, severed, and then concentrated prior to their analysis by mass spectrometry (MS)-based proteomics. We identified >90 unique mammalian proteins over multiple experimental trials from our preparations. Seventeen predicted molecules were significantly higher in abundance (p < 0.05) when compared to both the negative controls and sample means. Validation of two identified proteins (cyclophilin A [nonactin-associated] and transgelin [actin-associated]) by immunolocalization was used to confirm our analysis, and both showed enrichment at EPEC pedestals. The EPEC pedestal concentration technique developed here together with the identification of novel pedestal proteins not only provides a resource for the further characterization of molecular components within these structures but also demonstrates that EPEC pedestals can be used as a model system for the identification of novel functions of proteins not normally thought to be at actin-based structures.

Automatic classification of protein structures using physicochemical parameters.

Protein classification is the first step to functional annotation; SCOP and Pfam databases are currently the most relevant protein classification schemes. However, the disproportion in the number of three dimensional (3D) protein structures generated versus their classification into relevant superfamilies/families emphasizes the need for automated classification schemes. Predicting function of novel proteins based on sequence information alone has proven to be a major challenge. The present study focuses on the use of physicochemical parameters in conjunction with machine learning algorithms (Naive Bayes, Decision Trees, Random Forest and Support Vector Machines) to classify proteins into their respective SCOP superfamily/Pfam family, using sequence derived information. Spectrophores™, a 1D descriptor of the 3D molecular field surrounding a structure was used as a benchmark to compare the performance of the physicochemical parameters. The machine learning algorithms were modified to select features based on information gain for each SCOP superfamily/Pfam family. The effect of combining physicochemical parameters and spectrophores on classification accuracy (CA) was studied. Machine learning algorithms trained with the physicochemical parameters consistently classified SCOP superfamilies and Pfam families with a classification accuracy above 90%, while spectrophores performed with a CA of around 85%. Feature selection improved classification accuracy for both physicochemical parameters and spectrophores based machine learning algorithms. Combining both attributes resulted in a marginal loss of performance. Physicochemical parameters were able to classify proteins from both schemes with classification accuracy ranging from 90-96%. These results suggest the usefulness of this method in classifying proteins from amino acid sequences.

Automation Highlights from the Literature

Automation Highlights from the Literature

Alignment-Independent Comparison of Binding Sites Based on DrugScore Potential Fields Encoded by 3D Zernike Descriptors

Analyzing protein binding sites provides detailed insights into the biological processes proteins are involved in, e.g., into drug-target interactions, and so is of crucial importance in drug discovery. Herein, we present novel alignment-independent binding site descriptors based on DrugScore potential fields. The potential fields are transformed to a set of information-rich descriptors using a series expansion in 3D Zernike polynomials. The resulting Zernike descriptors show a promising performance in detecting similarities among proteins with low pairwise sequence identities that bind identical ligands, as well as within subfamilies of one target class. Furthermore, the Zernike descriptors are robust against structural variations among protein binding sites. Finally, the Zernike descriptors show a high data compression power, and computing similarities between binding sites based on these descriptors is highly efficient. Consequently, the Zernike descriptors are a useful tool for computational binding site analysis, e.g., to predict the function of novel proteins, off-targets for drug candidates, or novel targets for known drugs.

Development of the Gateway ® system for cloning and expressing genes in Entamoeba histolytica

The early branching eukaryote Entamoeba histolytica is a human parasite that is the etiologic agent of amebic dysentery and liver abscess. The sequencing of the E. histolytica genome combined with the development of an E. histolytica microarray has resulted in the identification of several distinct gene expression profiles associated with virulence. The function of many modulated transcripts is unknown and their role in pathogenicity is unclear. They however represent a pool of potential virulence factors that could be targets for the development of novel therapeutics. Efficient tools and methods to characterize these novel virulence-associated genes and proteins would be beneficial. Here we report the use of the Gateway ® cloning system to generate the E. histolytica expression vector pAH-DEST. To test the usefulness of this system, the vector was used to construct a plasmid containing a recombinant version of the locus EHI_144490, which encoded a protein of unknown function. The recombinant gene was expressed and the recombinant protein, which was strep–myc-tagged, showed a cytoplasmic localization in transfected trophozoites. This expression vector with the Gateway ® system should facilitate investigation into the functions of novel proteins in E. histolytica.

Finding finer functions for partially characterized proteins by protein-protein interaction networks

Based on high-throughput data, numerous algorithms have been designed to find functions of novel proteins. However, the effectiveness of such algorithms is currently limited by some fundamental factors, including (1) the low a-priori probability of novel proteins participating in a detailed function; (2) the huge false data present in high-throughput datasets; (3) the incomplete data coverage of functional classes; (4) the abundant but heterogeneous negative samples for training the algorithms; and (5) the lack of detailed functional knowledge for training algorithms. Here, for partially characterized proteins, we suggest an approach to finding their finer functions based on protein interaction sub-networks or gene expression patterns, defined in function-specific subspaces. The proposed approach can lessen the above-mentioned problems by properly defining the prediction range and functionally filtering the noisy data, and thus can efficiently find proteins’ novel functions. For thousands of yeast and human proteins partially characterized, it is able to reliably find their finer functions (e.g., the translational functions) with more than 90% precision. The predicted finer functions are highly valuable both for guiding the follow-up wet-lab validation and for providing the necessary data for training algorithms to learn other proteins.

Large scale analysis of protein‐binding cavities using self‐organizing maps and wavelet‐based surface patches to describe functional properties, selectivity discrimination, and putative cross‐reactivity

A new method to discover similar substructures in protein binding pockets, independently of sequence and folding patterns or secondary structure elements, is introduced. The solvent-accessible surface of a binding pocket, automatically detected as a depression on the protein surface, is divided into a set of surface patches. Each surface patch is characterized by its shape as well as by its physicochemical characteristics. Wavelets defined on surfaces are used for the description of the shape, as they have the great advantage of allowing a comparison at different resolutions. The number of coefficients to describe the wavelets can be chosen with respect to the size of the considered data set. The physicochemical characteristics of the patches are described by the assignment of the exposed amino acid residues to one or more of five different properties determinant for molecular recognition. A self-organizing neural network is used to project the high-dimensional feature vectors onto a two-dimensional layer of neurons, called a map. To find similarities between the binding pockets, in both geometrical and physicochemical features, a clustering of the projected feature vector is performed using an automatic distance- and density-based clustering algorithm. The method was validated with a small training data set of 109 binding cavities originating from a set of enzymes covering 12 different EC numbers. A second test data set of 1378 binding cavities, extracted from enzymes of 13 different EC numbers, was then used to prove the discriminating power of the algorithm and to demonstrate its applicability to large scale analyses. In all cases, members of the data set with the same EC number were placed into coherent regions on the map, with small distances between them. Different EC numbers are separated by large distances between the feature vectors. A third data set comprising three subfamilies of endopeptidases is used to demonstrate the ability of the algorithm to detect similar substructures between functionally related active sites. The algorithm can also be used to predict the function of novel proteins not considered in training data set.

FAST-NMR: functional annotation screening technology using NMR spectroscopy.

An abundance of protein structures emerging from structural genomics and the Protein Structure Initiative (PSI) are not amenable to ready functional assignment because of a lack of sequence and structural homology to proteins of known function. We describe a high-throughput NMR methodology (FAST-NMR) to annotate the biological function of novel proteins through the structural and sequence analysis of protein-ligand interactions. This is based on basic tenets of biochemistry where proteins with similar functions will have similar active sites and exhibit similar ligand binding interactions, despite global differences in sequence and structure. Protein-ligand interactions are determined through a tiered NMR screen using a library composed of compounds with known biological activity. A rapid co-structure is determined by combining the experimental identification of the ligand binding site from NMR chemical shift perturbations with the protein-ligand docking program AutoDock. Our CPASS (Comparison of Protein Active Site Structures) software and database are then used to compare this active site with proteins of known function. The methodology is demonstrated using unannotated protein SAV1430 from Staphylococcus aureus.

Evolutionary trace report_maker: a new type of service for comparative analysis of proteins

: Evolutionary trace report_maker offers a new type of service for researchers investigating the function of novel proteins. It pools, from different sources, information about protein sequence, structure and elementary annotation, and to that background superimposes inference about the evolutionary behavior of individual residues, using real-valued evolutionary trace method. As its only input it takes a Protein Data Bank identifier or UniProt accession number, and returns a human-readable document in PDF format, supplemented by the original data needed to reproduce the results quoted in the report.

Emerging challenges in ligand discovery: new opportunities for chromatographic assay

Ligand discovery initiatives are facing interesting challenges as ever-increasing numbers of proteins are entering screening programs. As an answer to steady pressure to improve performance in drug discovery, ligand discovery can expect to play an expanded role in generating small molecules as probes to help uncover the function of novel proteins. Chromatographic assay formats can offer new entry points into standard interaction characterization (binding and rate constants) as well as powerful, scaleable methods for compound screening. This review presents recent advancements in chromatographic assay technology, with a particular focus on frontal affinity chromatography as a platform technology for interaction analysis.

Rapid Combinatorial Screening of Peptide Libraries for the Selection of Lanthanide-Binding Tags (LBTs)

Elucidating the structures and functions of novel proteins and their interaction partners is a ubiquitous challenge in laboratories worldwide, escalating the demand for tools for protein study. A small, versatile and multifaceted co-expression tag would therefore be extremely useful to protein scientists: Lanthanide-Binding Tags (LBTs) fulfill such criteria. These tags are remarkably short peptide sequences (15 to 20 residues) comprising only the encoded amino acids; post-expression addition of a lanthanide ion endows the LBTs with the abundant spectroscopic properties of lanthanides. Herein is reviewed a powerful combinatorial screen developed expressly for the synthetic evolution of short calcium-binding motifs into selective, potent binders of terbium and related lanthanide ions. A series of seven libraries resulted in peptides with dissociation constants nearly three orders of magnitude tighter than that of the parent sequence. These libraries utilized two orthogonal linkers on the solid phase support, enabling cleaved peptides to be screened off-resin, while leaving a portion of the peptide intact on the synthesis resin for later sequencing. Terbium luminescence from the bound LBTs provides the positive selection criterion, and mass spectroscopy using limited capping was implemented for sequence deconvolution. The roles and importance of various residues within the later-generation LBTs are also discussed.

An SVM-based system for predicting protein subnuclear localizations

BackgroundThe large gap between the number of protein sequences in databases and the number of functionally characterized proteins calls for the development of a fast computational tool for the prediction of subnuclear and subcellular localizations generally applicable to protein sequences. The information on localization may reveal the molecular function of novel proteins, in addition to providing insight on the biological pathways in which they function. The bulk of past work has been focused on protein subcellular localizations. Furthermore, no specific tool has been dedicated to prediction at the subnuclear level, despite its high importance. In order to design a suitable predictive system, the extraction of subtle sequence signals that can discriminate among proteins with different subnuclear localizations is the key.ResultsNew kernel functions used in a support vector machine (SVM) learning model are introduced for the measurement of sequence similarity. The k-peptide vectors are first mapped by a matrix of high-scored pairs of k-peptides which are measured by BLOSUM62 scores. The kernels, measuring the similarity for sequences, are then defined on the mapped vectors. By combining these new encoding methods, a multi-class classification system for the prediction of protein subnuclear localizations is established for the first time. The performance of the system is evaluated with a set of proteins collected in the Nuclear Protein Database (NPD). The overall accuracy of prediction for 6 localizations is about 50% (vs. random prediction 16.7%) for single localization proteins in the leave-one-out cross-validation; and 65% for an independent set of multi-localization proteins. This integrated system can be accessed at .ConclusionThe integrated system benefits from the combination of predictions from several SVMs based on selected encoding methods. Finally, the predictive power of the system is expected to improve as more proteins with known subnuclear localizations become available.

The Structure and Function of Novel Proteins of Bacillus anthracis and Other Spore-Forming Bacteria: Development of Novel Prophylactic and Therapeutic Agents

The overall goal of this review is to summarize the current body of knowledge about the structure and function of major proteins of Bacillus anthracis and/or similar spore-forming organisms. B. anthracis is a key spore-forming biological threat agent, as well as human and animal Gram-positive bacterial pathogen. The structural information described here is limited to approximately the last 5 years. This information is then related to the role of the selected proteins in pathogenesis and in the possible development of novel vaccine and/or other antimicrobial agents against spore-forming organisms, including anthrax, a disease caused by B. anthracis.Among spore-forming bacteria, Bacillus and Clostridium species are the predominant spore-forming bacilli that cause serious diseases. The biochemical properties and mechanism of catalysis of the novel spore germination protease that degrades small, acid-soluble proteins protecting DNA against damage, a cofactor independent phosphoglycerate mutase, NAD+ synthetase, and the three know B. anthracis toxins, protective antigen, lethal factor, and edema factor are described. The studies described in this work review and unify selected information critical for the prevention of microbial diseases such as anthrax. A strategy for the structure-guided development of new prophylactic and therapeutic agents is discussed.

In vitro replication and mutagenesis of a novel reversion vector with selective DNA damage in the supF gene

The ability to detect the most common type of UV-induced mutation, the C to T transition, at the previously characterized hotspot at position 99 of the supF gene has been demonstrated in a selectively irradiated reversion vector, pLS189(Rev). The supF region was amplified, irradiated with 500 J/m 2 UVC or unirradiated, and ligated into the pLS189(Rev) plasmid. A portion of ligated product plasmid containing the irradiated fragment was sensitive to nicking by T4 endonuclease V, indicating the presence of the most common type of UV-induced damage, the pyrimidine dimer. Plasmid containing the irradiated or unirradiated supF gene was replicated completely in cellular extracts from either HeLa or XP-A cells in vitro. Plasmid containing the irradiated supF gene showed an inhibition of total replication to a level similar to those of previous studies with plasmid molecules exposed in their entirety to 40 J/m 2. Replication of selectively irradiated plasmid resulted in an average reversion frequency of 0.071% in the two extracts; a 42-fold increase over the average spontaneous reversion frequency of unirradiated plasmid. The reversion frequencies were not significantly different between extracts prepared from HeLa and XP-A cells, indicating that neither the repair status of the cell lines nor the XPA protein itself affect the frequency of C to T transitions at position 99 of the supF gene in plasmid replicated in vitro. These data indicate that the plasmid pSL189(Rev), containing the selectively UV-irradiated supF gene, is a useful and sensitive tool to study mutagenesis at a specific site. This approach may be applicable to the investigation of other environmental DNA-damaging agents, by allowing the target gene to be selectively damaged while maintaining the ability of the plasmid to replicate completely. Such a system, amenable to biochemical manipulation, may be very valuable in elucidating the function of novel proteins in the process of mutagenesis.

Evolutionary approaches to protein engineering.

The very term “protein engineering” remains something of an oxymoron, at least as far as engineering implies the rational application of well understood principles towards achieving a prespecified goal. Designed novel functions of proteins remain largely beyond our capabilities, despite intense efforts of numerous research groups in academia and industry. But we are trying, and the last few years have seen a rapid growth in the number of reports describing the successful application of a novel, and at the same time ancient, principle to the problem: evolutionary protein engineering. While this sophisticated trial-and-error approach may at first appear less rational than crystal structure gazing, it is undisputedly more successful — and there is nothing irrational about experimental success. This chapter will focus on the principles, concepts and methods of this field.KeywordsGreen Fluorescent ProteinSequence SpaceEvolutionary ApproachPhage DisplayProtein EngineeringThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.